针对手臂静脉这一生物特征，提出一种基于链码表示的静脉特征提取及匹配算法。由近红外手臂图像中提取出静脉的骨架结构，并将其分割为若干条曲线段，通过曲线的相对方向、相对位置及形状特征计算匹配曲线对，利用粒子群算法计算匹配曲线间的最优空间变换关系，根据静脉全局变换后点集的重叠情况判断匹配程度。针对来自9个国家的110位实验者组成的手臂图像库进行实验，Rank-1和Rank-10%识别率分别为74.5%和93.6%，优于改进Hausdorff距离及模板匹配方法，表明手臂静脉可作为一种新的生物特征来进行身份认证。

A feature extraction and matching algorithm is proposed based on chain code to study the arm vein. The skeleton structure of the vein is extracted from the near infrared images of the arm and then divided into several curve segments. Matched curve pairs are calculated based on the relative direction, relative location and shape features of curves, and then the spatial transformation between the matched curve pairs is obtained with the particle swarm optimization algorithm. The matching probability is calculated based on the overlapping ratio of all the transformed vein points. The experiment on a database composed of arm images of 110 subjects from 9 countries shows that the identification rates for rank-1 and rank-10% are 74.5% and 93.6%, respectively, which is superior to the results obtained with algorithms of modified Hausdorff distance and template matching. It indicates that arm veins can be used as a new biometric feature for identity recognition.